


import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 				__lowercase  (					__lowerCAmelCase				:							List[str]  ):
							a__			 =	SwinConfig()
							a__			 =	swin_name.split('_'  )

							a__			 =	name_split[1]
							a__			 =	int(name_split[4]  )
							a__			 =	int(name_split[3][-1]  )

							if model_size == "tiny":
														a__			 =	9_6
														a__			 =	(2, 2, 6, 2)
														a__			 =	(3, 6, 1_2, 2_4)
							elif model_size == "small":
														a__			 =	9_6
														a__			 =	(2, 2, 1_8, 2)
														a__			 =	(3, 6, 1_2, 2_4)
							elif model_size == "base":
														a__			 =	1_2_8
														a__			 =	(2, 2, 1_8, 2)
														a__			 =	(4, 8, 1_6, 3_2)
							else:
														a__			 =	1_9_2
														a__			 =	(2, 2, 1_8, 2)
														a__			 =	(6, 1_2, 2_4, 4_8)

							if "in22k" in swin_name:
														a__			 =	2_1_8_4_1
							else:
														a__			 =	1_0_0_0
														a__			 =	"""huggingface/label-files"""
														a__			 =	"""imagenet-1k-id2label.json"""
														a__			 =	json.load(open(hf_hub_download(__lowerCAmelCase   ,       __lowerCAmelCase   ,       repo_type='dataset'  )   ,       'r'  )  )
														a__			 =	{int(__lowerCAmelCase  ): v for k, v in idalabel.items()}
														a__			 =	idalabel
														a__			 =	{v: k for k, v in idalabel.items()}

							a__			 =	img_size
							a__			 =	num_classes
							a__			 =	embed_dim
							a__			 =	depths
							a__			 =	num_heads
							a__			 =	window_size

							return config






def 				__lowercase  (					__lowerCAmelCase				:							Dict  ):
							if "patch_embed.proj" in name:
														a__			 =	name.replace('patch_embed.proj'   ,       'embeddings.patch_embeddings.projection'  )
							if "patch_embed.norm" in name:
														a__			 =	name.replace('patch_embed.norm'   ,       'embeddings.norm'  )
							if "layers" in name:
														a__			 =	"""encoder.""" + name
							if "attn.proj" in name:
														a__			 =	name.replace('attn.proj'   ,       'attention.output.dense'  )
							if "attn" in name:
														a__			 =	name.replace('attn'   ,       'attention.self'  )
							if "norm1" in name:
														a__			 =	name.replace('norm1'   ,       'layernorm_before'  )
							if "norm2" in name:
														a__			 =	name.replace('norm2'   ,       'layernorm_after'  )
							if "mlp.fc1" in name:
														a__			 =	name.replace('mlp.fc1'   ,       'intermediate.dense'  )
							if "mlp.fc2" in name:
														a__			 =	name.replace('mlp.fc2'   ,       'output.dense'  )

							if name == "norm.weight":
														a__			 =	"""layernorm.weight"""
							if name == "norm.bias":
														a__			 =	"""layernorm.bias"""

							if "head" in name:
														a__			 =	name.replace('head'   ,       'classifier'  )
							else:
														a__			 =	"""swin.""" + name

							return name






def 				__lowercase  (					__lowerCAmelCase				:							List[str]   ,       __lowerCAmelCase				:							Optional[Any]  ):
							for key in orig_state_dict.copy().keys():
														a__			 =	orig_state_dict.pop(__lowerCAmelCase  )

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					a__			 =	key.split('.'  )
																					a__			 =	int(key_split[1]  )
																					a__			 =	int(key_split[3]  )
																					a__			 =	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												a__			 =	val[:dim, :]
																												a__			 =	val[
																												    dim : dim * 2, :
																												]
																												a__			 =	val[-dim:, :]
																					else:
																												a__			 =	val[
																												    :dim
																												]
																												a__			 =	val[
																												    dim : dim * 2
																												]
																												a__			 =	val[
																												    -dim:
																												]
														else:
																					a__			 =	val

							return orig_state_dict






def 				__lowercase  (					__lowerCAmelCase				:							List[Any]   ,       __lowerCAmelCase				:							Tuple  ):
							a__			 =	timm.create_model(__lowerCAmelCase   ,       pretrained=__lowerCAmelCase  )
							timm_model.eval()

							a__			 =	get_swin_config(__lowerCAmelCase  )
							a__			 =	SwinForImageClassification(__lowerCAmelCase  )
							model.eval()

							a__			 =	convert_state_dict(timm_model.state_dict()   ,       __lowerCAmelCase  )
							model.load_state_dict(__lowerCAmelCase  )

							a__			 =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

							a__			 =	AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'   ,       '-'  )  )  )
							a__			 =	Image.open(requests.get(__lowerCAmelCase   ,       stream=__lowerCAmelCase  ).raw  )
							a__			 =	image_processor(images=__lowerCAmelCase   ,       return_tensors='pt'  )

							a__			 =	timm_model(inputs['pixel_values']  )
							a__			 =	model(**__lowerCAmelCase  ).logits

							assert torch.allclose(__lowerCAmelCase   ,       __lowerCAmelCase   ,       atol=1E-3  )

							print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'  )
							model.save_pretrained(__lowerCAmelCase  )

							print(F'Saving image processor to {pytorch_dump_folder_path}'  )
							image_processor.save_pretrained(__lowerCAmelCase  )


if __name__ == "__main__":
						snake_case				:			Union[str, Any]	     = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swin_name''',
						    default='''swin_tiny_patch4_window7_224''',
						    type=str,
						    help='''Name of the Swin timm model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)

						snake_case				:			List[Any]	     = parser.parse_args()
						convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






